Cannot run tensorflow on GPU

Question

I want to run tensorflow code on my GPU but its not working. I have Cuda and cuDNN installed and have a compatible GPU as well.

I took this sample from the official website tutorial for GPUs here Tensorflow tutorial for GPU

# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

Here is my output of it:

Device mapping: no known devices.
2017-10-31 16:15:40.298845: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping:

MatMul: (MatMul): /job:localhost/replica:0/task:0/cpu:0
2017-10-31 16:15:56.895802: I tensorflow/core/common_runtime/simple_placer.cc:872] MatMul: (MatMul)/job:localhost/replica:0/task:0/cpu:0
b: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-10-31 16:15:56.895910: I tensorflow/core/common_runtime/simple_placer.cc:872] b: (Const)/job:localhost/replica:0/task:0/cpu:0
a_1: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-10-31 16:15:56.895961: I tensorflow/core/common_runtime/simple_placer.cc:872] a_1: (Const)/job:localhost/replica:0/task:0/cpu:0
a: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-10-31 16:15:56.896006: I tensorflow/core/common_runtime/simple_placer.cc:872] a: (Const)/job:localhost/replica:0/task:0/cpu:0
[[ 22.  28.]
 [ 49.  64.]]

There is no option for running on my GPU. I tried to force it to run on GPU manually using this:

with tf.device('/gpu:0'):
...

It gave a bunch of errors:

Traceback (most recent call last):
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
    return fn(*args)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1297, in _run_fn
    self._extend_graph()
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1358, in _extend_graph
    self._session, graph_def.SerializeToString(), status)
  File "/home/abhor/anaconda3/lib/python3.6/contextlib.py", line 88, in __exit__
    next(self.gen)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'MatMul_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/cpu:0 ]. Make sure the device specification refers to a valid device.
     [[Node: MatMul_1 = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/device:GPU:0"](a_2, b_1)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
    options, run_metadata)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'MatMul_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/cpu:0 ]. Make sure the device specification refers to a valid device.
     [[Node: MatMul_1 = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/device:GPU:0"](a_2, b_1)]]

Caused by op 'MatMul_1', defined at:
  File "<stdin>", line 4, in <module>
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 1844, in matmul
    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 1289, in _mat_mul
    transpose_b=transpose_b, name=name)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/abhor/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'MatMul_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/cpu:0 ]. Make sure the device specification refers to a valid device.
     [[Node: MatMul_1 = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/device:GPU:0"](a_2, b_1)]]

I see that in some lines it says only CPU is available.

Here are my graphic card details and Cuda versions.

Output for nvidia-smi:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.81                 Driver Version: 384.81                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce 940MX       Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   43C    P0    N/A /  N/A |    274MiB /  2002MiB |     10%      Default |
+-------------------------------+----------------------+----------------------+

Output for nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

I don't know how to check for cuDNN, but I installed it the way it was given in the official documentation, so I am guessing it should be working as well.

EDIT: Output for pip3 list | grep tensorflow

tensorflow-gpu (1.3.0)
tensorflow-tensorboard (0.1.8)

Can you add the output of the command `pip list | grep tensorflow`? — mrry, Oct 31 '17 at 19:55
Most probably, there were some compatibility issues due to old packages. I tried installing on a fresh Ubuntu system and it worked. — Abhor, Apr 01 '18 at 09:20

score 13 · Answer 1 · edited Jun 25 '19 at 07:45

13

Try this piece of code:

sess = tf.Session(config=tf.ConfigProto(
      allow_soft_placement=True, log_device_placement=True))

edited Jun 25 '19 at 07:45

Jasurbek

2,946
3
20
37

answered Oct 13 '18 at 11:11

Luís Carlos Silva Eiras

131
1
4

1

Could you please explain why does it works ? – Cryckx Mar 24 '21 at 18:35

score 2 · Answer 2 · answered Jun 25 '19 at 07:15

Actually tensorflow cannot find the CUDA GPU in your situation.

Refer to the output device list there:

Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/cpu:0 ]

This means no GPU is found. You can referring to codes here from How to get current available GPUs in tensorflow?, to list GPU (which tensorflow actually can find).

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

You must make sure actually found gpu/s is returned, thus tensorflow can use the gpu device.

There are many possibilities that gpu cannot be found, including but not limited, CUDA installation/settings, tensorflow versions and GPU model especially the GPU compute capability. Must checkout the tensorflow version support for a certain GPU model, and must checkout the GPU capability (for NVidia GPUs).

score 0 · Answer 3 · answered Aug 29 '18 at 14:25

In general my recommendation is to use conda environments. In this case you can create a new fresh environment and try to install tensorflow or any other tool from scratch without reinstall whole os. As added value you can have more then environment on PC

Cannot run tensorflow on GPU

3 Answers3